Systems and methods for rating and pricing insurance policies

ABSTRACT

Systems, methods, apparatus, computer program code and means for rating and pricing insurance policies are provided. In some embodiments, an automated insurance processing platform rates and prices insurance policies by including a territory factor in the calculation of a premium for the policy. Pursuant to some embodiments, the territory factor is calculated by receiving historical loss data, geographical data, and demographic data, analyzing the historical loss data, the geographical data, and the demographic data to identify data having similar claim behaviors. The historical loss data is analyzed to identify at least a frequency and severity of historical loss by coverage type. The frequency and severity of loss data, the geographical data, and the demographic data is iteratively analyzed to create a territory set having different geographical boundaries for the different coverage types; and the territory set is used to generate a set of territory factors for the different coverage types and the territories.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of and priority to provisionalpatent application Ser. No. 61/095,785, filed Sep. 10, 2008, thecontents of which are hereby incorporated by reference herein in theirentirety.

BACKGROUND

Small and medium size businesses span a wide range of business types,and involve a wide range of business risks and risk characteristics,making it difficult to generate and analyze information to producerating and pricing policies that can be reliably and consistentlyapplied to different businesses in different geographical locations.

The rating and pricing of business insurance policies is complex, and ismade particularly complex by the wide range of different types andclasses of businesses. Rating and pricing is made even more complex bythe different geographical, demographic and even environmentalconditions that are relevant to the risk of loss for differentbusinesses. For example, certain areas of the U.S. present higher lossrisks due to catastrophic conditions such as hurricanes or floods. Asanother example, certain areas present higher loss risks due to theft.Current rating and pricing systems do not adequately take suchterritorial variations into consideration when pricing and evaluatingbusiness insurance policies.

It would be desirable to provide systems and methods for rating andpricing insurance policies which achieve better rate and pricingspecificity and flexibility. It would further be desirable to provideestablished base rates by coverage, amount of insurance relativities,territories or geographical location. It would be further desirable toprovide systems and methods that allow existing agent systems andprocesses to quickly and efficiently price and quote business ownerinsurance policies using the improved rating and pricing systems.

SUMMARY OF THE INVENTION

According to some embodiments, systems, methods, apparatus, computerprogram code and means for rating and pricing insurance policies areprovided. In some embodiments, an automated insurance processingplatform rates and prices insurance policies by including a territoryfactor in the calculation of a premium for the policy. Pursuant to someembodiments, the territory factor is calculated by receiving historicalloss data, geographical data, and demographic data, analyzing thehistorical loss data, the geographical data, and the demographic data toidentify data having similar claim behaviors. The historical loss datais analyzed to identify at least a frequency and severity of historicalloss by coverage type. The frequency and severity of loss data, thegeographical data, and the demographic data is iteratively analyzed tocreate a territory set having different geographical boundaries for thedifferent coverage types; and the territory set is used to generate aset of territory factors for the different coverage types and theterritories.

According to some embodiments, a quoting process using the rating sheetsgenerated pursuant to some embodiments includes receiving a quoterequest associated with a business, the quote request specifying abusiness type and a business location, identifying at least firstapplicable coverage, based on said at least first applicable coverage,identifying at least a first relevant coverage formula, querying arating database using said at least first relevant coverage formula,said business type and said business location, said query resulting inat least a first price for said at least first applicable coverage, andtransmitting a response to said quote request, said response includingsaid at least first price and said at least first applicable coverage.

Other embodiments include: generating a net premium for an insurancepolicy using the net premium of the predominant location on the policy,calculating a premium for an optional coverage as the product of thepolicy net rate and the optional coverage amount, and displaying thepremium for the optional coverage to at least one of an agent and aninsured.

Some embodiments include: the calculation of a policy expense feeassociated with a quote request, the calculation including thedetermination of a likelihood of a business incurring an administrativeexpense, the calculation (based on the likelihood) of a policy expensefee, and the transmission of the policy expense fee with a response tothe quote request.

In some embodiments, a communication device associated with an automatedinsurance processing platform exchanges information with remote devices.The information may be exchanged, for example, via public and/orproprietary communication networks.

A technical effect of some embodiments of the invention is an improvedand computerized insurance rating and quoting system providing improvedrate and pricing specificity and flexibility for business insurancepolicies. With these and other advantages and features that will becomehereinafter apparent, a more complete understanding of the nature of theinvention can be obtained by referring to the following detaileddescription and to the drawings appended hereto.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is block diagram of a system according to some embodiments of thepresent invention.

FIG. 2 illustrates a method according to some embodiments of the presentinvention.

FIGS. 3A-3B illustrate a further method according to some embodiments ofthe present invention.

FIG. 4 illustrates a further method according to some embodiments of thepresent invention.

FIG. 5 illustrates a further method according to some embodiments of thepresent invention.

FIG. 6 illustrates a further method according to some embodiments of thepresent invention.

FIG. 7 is a block diagram of an insurance apparatus in accordance withsome embodiments of the present invention.

FIGS. 8 a-8 b illustrate graphical depictions of portions of territorymaps in accordance with some embodiments of the present invention.

FIG. 9 illustrates an input data table used in selecting territorygroupings according to some embodiments of the present invention.

FIG. 10 illustrates a chart for analyzing territory groupings inaccordance with some embodiments of the present invention.

DETAILED DESCRIPTION

FIG. 1 is block diagram of an insurance system 100 according to someembodiments of the present invention. The system 100 may, for example,facilitate the creation of rating schedules for business insurancepolicies as well as perform the quoting, rating and pricing ofindividual policies using those rating schedules. According to someembodiments, an “automated” insurance processing platform 110 may beprovided. As used herein the term “automated” indicates that at leastsome part of a step associated with a process or service is performedwith little or no human intervention. By way of examples only, theplatform 110 may be associated and/or communicate with a PersonalComputer (PC), an enterprise server, a database farm, and/or a consumerdevice. The automated insurance processing platform 110 may, accordingto some embodiments, apply rating schedules, and price and rateindividual business policies using those rating schedules. Pursuant tosome embodiments, the rating schedules include one or more territoryfactors used to incorporate geographical, demographic and territorialloss data into the rating and pricing of policies.

As shown, the automated insurance processing platform 110 may include anumber of modules or components, including one or more analysis modules112, quoting modules 114 and issuing modules 116. As will be describedfurther below, the analysis modules 112 may be used in conjunction withthe creation and updating of one or more rating schedules for use inpricing and rating business insurance policies pursuant to embodimentsof the present invention. For example, in some embodiments, the analysismodules 112 are used to analyze historical loss data in conjunction withgeographic and demographic data to generate territory factors for use inrating and pricing business insurance policies. In some embodiments, aswill be described further below, the territory factors are used inconjunction with one or more rating formulas to allow improved pricingand analysis of business insurance policies. The analysis modules 112may include code or other modules used to analyze and generate expensefees, analyze and create rating factors, analyze and create territoryfactors, and perform pricing analysis. The analysis modules 112 may beoperated to create data for storage and use in one or more ratingdatabases 120. The data stored in rating databases 120 are accessed bythe automated processing platform 110 to allow quoting and issuing ofpolicies using the ratings data.

The quoting modules 114 may be used in conjunction with the quoting,rating and pricing of individual business insurance policies (e.g., inresponse to requests for quotes received from agents operating agentdevices 130).

The automated insurance processing platform 110 and the analysis modules112 may access information in one or more databases 117-118. Thedatabases may include, for example, risk characteristic data 117 andhistorical loss data 118 associated with previously-issued insurancepolicies. As will be described further below, the risk characteristicdata 117 and the historical loss data 118 may be used by the analysismodules 112 in the creation and updating of rating schedules for thestorage in one or more rating databases 120 for use by the processingplatform 110 in quoting, pricing and issuing new business insurancepolicies.

The automated insurance processing platform 110 and the analysis modules112 may also have access to data from one or more external data sources140. The external data sources 140 may include data used, for example,by the analysis modules 112 in the analysis and generation of ratingtables. For example, external data sources 140 providing demographic,geographic, and climate data may be accessed by the automated insuranceprocessing platform 110. In some embodiments, the external data sources140 may include publicly accessible data (e.g., such as U.S. zip codedata, and U.S. census data).

The automated insurance processing platform 110 might access thedatabases 117-120 and the external data sources 140 via one or morecommunication networks. These devices (and any of the other devicesdescribed herein) could be associated with, for example, a server, a PC,a mobile or laptop computer, or any other appropriate storage and/orcommunication device to exchange information via a web site and/or acommunication network. As used herein, devices (including thoseassociated with the automated insurance processing platform 110, thedatabases 117-120, the external data sources 140, the agent devices 130and any other device described herein) may exchange information via anycommunication network, such as a Local Area Network (LAN), aMetropolitan Area Network (MAN), a Wide Area Network (WAN), aproprietary network, a Public Switched Telephone Network (PSTN), aWireless Application Protocol (WAP) network, a Bluetooth network, awireless LAN network, and/or an Internet Protocol (IP) network such asthe Internet, an intranet, or an extranet. Note that any devicesdescribed herein may communicate via one or more such communicationnetworks.

The automated insurance processing platform 110 and/or databases 117-120may be, according to some embodiments, accessible via a Graphical UserInterface (GUI). The GUI might be used, for example, to dynamicallydisplay existing insurance policy information, analyze historical ordemographic data to generate territory factors for use in ratingformulas, generate updated or new rating tables, receive requests forbusiness insurance policy information, and/or to associate one or morecost of insurance rates with an existing or proposed policy.

Although a single automated insurance processing platform 110 is shownin FIG. 1, any number of such devices may be included. Moreover, variousdevices described herein might be combined according to embodiments ofthe present invention. For example, in some embodiments, the automatedinsurance processing platform 110 and databases 117-120 might beco-located and/or may comprise a single apparatus. In some embodiments,the analysis modules 112, and the analysis and generation of ratingsfactors (such as the territory rating factors described below) isperformed using one or more separate systems. In some embodiments, someor all of the analysis may be performed using a spreadsheet or otheranalytic program.

FIG. 2 illustrates a method that might be performed, for example, bysome or all of the elements of the system 100 described with respect toFIG. 1 according to some embodiments. The flow charts described hereindo not imply a fixed order to the steps, and embodiments of the presentinvention may be practiced in any order that is practicable. Note thatany of the methods described herein may be performed by hardware,software, or any combination of these approaches. For example, acomputer-readable storage medium may store thereon instructions thatwhen executed by a machine result in performance according to any of theembodiments described herein.

The process 200 may be performed to generate (or update) a ratingsdatabase to allow the quoting, pricing and issuance of businessinsurance policies using features of the present invention. Pursuant tosome embodiments, process 200 involves processing at 202 where ratingsfactors (including territory factors) and base premium amounts arecalculated and generated, processing at 204 where the ratings databaseis updated with those factors and premiums, and processing at 206 wherea set of formulas is created and stored for use in accessing the ratingsdata for use in the pricing of business insurance policies. In someembodiments, process 200 may be used to create ratings data for eachregulated territory or region. As a specific example, where the process200 is used to create ratings data for a State in the United States,process 200 is performed on a State-by-State basis to produce ratingsdata and formulas for the issuance and quoting of policies in a singleState. As will be discussed further below, the individual zip codeswithin a State are analyzed to create territory factors for ratinginsurance policies within the State.

According to some embodiments, the creation of a ratings databaseinvolves the generation of a number of rating factors and premiums. Forexample, to allow the calculation of a property liability insurancepremium for a business, the following rating factors may be generatedand stored in the ratings database: market group factors, class factors,amount of insurance factors, protection class factors, constructionclass factors, building age factors, number of locations factors, etc.Some of these factors may be created using traditional techniques usedin the art. Pursuant to some embodiments, additional factors, referredto herein as territory factors, are provided which are created usingtechniques of the present invention. These territory factors allow moreaccurate and predictive pricing of business insurance premiums, and maybe created using the process described below in conjunction with FIG. 3.

Process 200 results in the generation of ratings data (or tables) whichmay be accessed or used in the pricing of business insuranceapplications using a set of formulas selected based on a policy type.For example, an application for a property liability insurance policymay result in the execution of a building rating formula which retrievesdata from ratings database. An example of a portion of a building ratingformula is: Calculated Premium=[Base Premium]*[Market GroupFactor]*[Class Factor]*[Territory Factor].

In the sample building rating formula shown above, each of the variables(contained in brackets) are retrieved from the ratings database (such asthe database 120 of FIG. 1) based on information stored in the ratingsdatabase in process 200. Those skilled in the art, upon reading thisdisclosure, will appreciate that a number of different types of ratingformulas may be used. For example, the system 100 may use some or all ofthe following rating formulas to access ratings data from ratingsdatabase 120 to price business insurance policies: a building ratingformula, a business personal property rating formula, a personalproperty of others rating formula, products rating formulas, PREM/OPSrating formulas, business interruption ratings formulas, an equipmentbreakdown rating formula, a policy expense fee rating formula, and oneor more optional coverage rating formulas.

Some or all of the steps of process 200 may be repeated as needed (e.g.,on a regular or scheduled basis) to ensure that the ratings data isup-to-date and accurately reflects risk and pricing conditions, and toensure that appropriate and relevant formulas are used in the operationof the system.

Applicants have recognized that territory or geographical conditions maybe used to improve the pricing and analysis of business insurancepolicies. The generation, smoothing and use of territory factorspursuant to some embodiments will now be described by reference to FIG.3. Pursuant to some embodiments, a set of territories (or geographicalregions) are created which are assigned a territory rating factor foruse in the ratings database 120 of FIG. 1. In some embodiments, the setof territories is based on zip codes within a State being analyzed. Aswill be described further below, groups or sets of zip codes havingsimilar characteristics are grouped together and assigned similarterritory factors. The number of zip codes within a grouping or set isselected to improve the predictive power and to reduce statisticalnoise, as well as to increase the perceived or actual fairness ofratings within a geographical region. Pursuant to some embodiments, theterritorial data used in generating the territory factors is generatedusing an analysis of geographic (zip code), demographic, and other data.Pursuant to some embodiments, a set of rating boundaries is created byState (or other geographic region). In one embodiment, the ratingboundaries are based on zip code regions and encompass contiguousgeographic areas. Those skilled the art will appreciate that otherboundary regions may also be used.

In some embodiments, a result of the territory analysis is a set ofterritory factors (for use in rating formulas, and stored in a ratingdatabase or table) with a set of territories having territoryboundaries. In some embodiments, the same territory boundaries willapply to all classes of businesses (e.g., the same territory will beused to identify the rating information for a manufacturing business aswell as a retail business). Pursuant to some embodiments, historicalloss data is analyzed (by zip code, for example) to determine a set ofboundaries having reliably grouped zip codes with the most similar riskof loss within a territory. The analysis, in some embodiments, attemptsto identify the greatest differences between territories (e.g., so thatthe most similar risk of losses are within the same territories).

Pursuant to some embodiments, separate territory factors may begenerated for liability insurance, property insurance, and insurancebased on catastrophe-related losses (such as floods, hurricanes, etc.).In such embodiments, separate ratings tables, with separate territoryfactors, may be created and stored in ratings database 120 for access bythe quoting module 114 in the quoting and pricing of new policies. Theterritory factors may be generated using a process such as the processdescribed below in conjunction with FIGS. 3A and 3B.

The processing of FIG. 3A begins at 302 where the automated processingplatform 102 receives (or otherwise obtains) historical loss data (e.g.,from database 118), geographic data (e.g., from external data source140) and demographic data (e.g., from external data source 140) foranalysis. Data received at 302, in some embodiments, also includesoutputs from predictive models (e.g., such as generalized linear models,or “GLMS”) which are run to predict the statistical likelihood of futureclaims or losses based on past data (such as the historical loss data).GLMS may be used to predict claim frequency, claim severity or purepremium. The historical loss data may be segmented into both frequencyof loss and severity of loss data. The frequency and severity data, insome embodiments, may be obtained from historical loss data and may besupplemented using external data.

For example, the following external demographic, geographic, and climatedata may be used to supplement historical property coverage information:population and population density data, percentage of populationmarried, percentage of population with college education, percentage ofpopulation using public transportation, percentage of buildings builtbefore a certain date (e.g., such as 1960), crime rate, weather data,including rainfall, snowfall, and temperature data. This externaldemographic, geographic and climate data may be obtained, for example,from a number of publicly available data sources. Pursuant to someembodiments, the territory data is based on U.S. zip code data (althoughdata from other sources or regions may also be used).

The outputs from the predictive models may further include dataidentifying one or more significant geo variables which are identifiedto reduce the geographical or demographic influence on claim or lossdata. For example, there are more thefts where the crime rate is highand more slip and fall claims occur where population density is highestand/or sidewalks are icy. Other significant geo variables may includethe percentage of businesses in a zip code making a claim, the crimerate in a zip code, or the like. The significant geo variables may bebased on zip codes within a territory or region being analyzed (e.g.,such as a State). The GLMS may also be operated to perform an analysisof the frequency and severity of historical loss data by coverage type(e.g., where “coverage type” may include property coverage or “prem/ops”coverage), by peril of loss for property coverage (e.g., where perilsinclude theft, fire, water, non-hurricane wind, hurricane wind, andother), and by type of exposure base for prem/ops coverage (such assales numbers, payroll size, and property square footage).

Processing continues at 304 where the loss data received at 302 isadjusted to remove the effects of risk characteristics and thesignificant geo variables. That is, processing at 304 includes removingcertain variables having an impact on the loss data which are associatedwith geographic and other variables.

Processing continues at 306 where the dataset created at 304 isprocessed using a residual smoothing procedure to account for knownvariables and remove items not driven by generic risk characteristics ona geographic basis. This smoothing process is performed by territory(e.g., by State). For example, in some embodiments, processing at 306includes generating exposure data by zip code, and then performing aresidual smoothing process on the exposure data to improve the data(e.g., by removing residual or statistical noise) for further analysis.In some embodiments, an algorithm such as Formula 1 shown below, may beused, although those skilled in the art, upon reading this disclosure,will recognize that other formulas may be used.

$\begin{matrix}{S_{i} = {\sum\limits_{d_{j} \leq z}^{\;}\frac{U_{j}}{d_{j}^{y}}}} & {{Formula}\mspace{14mu} 1}\end{matrix}$

Where the smoothed Si is the sum of all the unsmoothed U_(j)'s dividedby dj (the distance from zip code (i) to zip code 0)) with parameters yand z. Formula 1 may be applied for a series of zip codes from (i) to(j), where U_(i) is a loss cost or risk characteristic in a zip codearea (i). By performing this smoothing on the loss data associated witheach zip code, a smoothed data set is produced with noise or otheranomalies removed. The smoothed data set is then combined with thesignificant geo variables (that were removed at 304) at 308.

Processing continues at 310 where an iterative clustering analysis isperformed on the data created at 308. In general, processing at 310involves iteratively grouping or clustering the zip code data for theState or region being analyzed to produce groups of zip codes that havesimilar loss or exposure characteristics. By grouping zip codes in thismanner, a reduced number of potential territories are created whichprovide improved pricing and analysis of business insurance policies.Applicants have recognized that a number of benefits arise from suchgroupings. For example, processing overhead is reduced by having fewerterritories. As another example, confusion in pricing is avoided. As aspecific example, if territories were not grouped pursuant to thepresent invention, situations may arise where a business in one zip codearea may enjoy a low premium, while a similar business in a second zipcode (which may be geographically very near the first zip code) receivesa second premium, different than the first premium. Such pricingdiscontinuities may be difficult to explain and may not accuratelyrepresent the exposure difference.

Pursuant to some embodiments, the creation and selection of territorygroupings is performed in an iterative process as shown in FIG. 3B. Forexample, the geographical, demographic and loss data may be analyzed inmultiple scenarios, with each scenario involving a different grouping ofzip codes. Each scenario may include a different sized grouping ofterritories, ranging from about 5 territories to fifty or moreterritories. Each scenario is then analyzed to determine the appropriatesized grouping. For example, the most appropriate sized grouping may beone having sufficient predictive power (e.g., which could occur when toofew territory groups are used) and less statistical noise (e.g., whichcould occur when too many territory groups are used). One process forgrouping territories will now be described by reference to FIG. 3B inconjunction with FIGS. 9 and 10.

The process of FIG. 3B begins at 352 with the loading of zip code data,exposure data and loss data (e.g., from FIG. 3A, step 308). FIG. 9 is atable 900 showing a part of an input data set that may be used in theiterative clustering analysis and represents some of the input dataloaded at 352. The table 900 includes a number of columns (including azip code column, an exposures in zip code column, a smoothed dataexposures column, and a set of one or more zip code neighbors columns).The data in the zip code column may include all zip codes for a State orother region that is being analyzed, and may be obtained from one ormore public data sources. The data in the exposures columns may beobtained from historical loss data sources (e.g., such as historicalloss database 118 of FIG. 1), and the smoothed data may be the datagenerated at step 308. The zip code neighbors are those zip codes thatare geographically next to the zip code in the first column of thetable, and may be obtained from public data sources.

The data in the table 900 is analyzed to generate clusters, or groups ofterritories. As an initial matter, a range of zip code clusters may beselected for testing and grouping. The range of clusters to be tested isset at step 356 of FIG. 3B. The size of the range may be based on thesize of the State or region being analyzed. For example, a range of 5-40zip codes may be used. The analysis then proceeds in two or moreiterations.

Process 350 continues with a first clustering iteration at 358. Thefirst clustering iteration proceeds as follows. First, the input data(from table 900) is loaded into an analysis macro or tool, and a minimumthreshold exposure for each cluster and a minimum number of zip codes isselected. For example, a minimum exposure of 2% and a minimum number of10 zip codes may be selected. Next, a copy of the data from table 900 ismade, and each zip code is marked, flagged, or otherwise designated as a“cluster”. Next, the data is reduced by merging (e.g., one at a time)the two most similar neighboring clusters that are neighbors where atleast one is in a cluster that does not meet at least one of theminimums set above. This continues until all of the clusters meet theminimums set at 354. The first iteration continues by reducing the twomost similar neighboring clusters (one at a time) until the clustercount is within the input range of clusters to be tested. The output ofthis iteration is stored in memory, and the second iteration isperformed. To “reduce” the most similar neighboring clusters, areduction algorithm may be performed which adds two cluster exposureamounts together, weights two cluster average loss costs together usingexposure data, and then combines two distinct cluster neighbors. Theresult of this first iteration are stored as an interim result at 360,and the original data (from 352) is restored for further processing.

Processing continues at 362 where an increment is established (shown as“X” in FIG. 3B). The increment, for example, may be set as “3” (wherethe clusters will be reduced 3 times). Several other processingvariables may also be set at 362. For example, a variable such as “N”may be set to equal the total number of zip codes to be processed (or,the total number of zip code areas in the State being analyzed). Acounter variable (shown as “n”) may also be set to 1.

Processing continues at 364 where the clusters are reduced in asubsequent iteration to reduce the clusters by the set increment (in afirst iteration, the clusters are reduced n*X times, or 1*3 times in theexample). Each subsequent iteration begins with the original data (e.g.,the data from table 900). In performing the subsequent iteration, theset increment is used (in the example, the increment is set as 3, so thetotal number of zip codes in the State is divided by 3). Each of the zipcodes in the table 900 is set as a cluster, and n*X clusters should bereduced by merging (one at a time) the two most similar neighboringclusters without regard to the minimums set forth in 354. The data isthen reduced by merging (one at a time) the two most similar neighboringclusters where at least one is in a cluster that does not meet at leastone of the minimums set in 354. This continues until the cluster countis within the input range of clusters to be tested. Once the input rangeis reached, the output of the first iteration (stored at 360) iscompared to the output of the subsequent iteration (at 366) to determinewhich output is more desirable, and the selected output is stored as thecurrently preferred set.

The reduction may continue until n=N (that is, the reduction at 365 maybe repeated N/X times). Each iteration results in the storage of interimresults and the comparison to the previous “best” clustering at 366until the best clustering from the different iterations is obtained. Atthe end of each iteration, the original data is restored for use by thesubsequent iteration. The best clustering may be stored in spreadsheetfor further analysis and approval by an administrator.

Further analysis and approval of the clustering may be performed bydividing an expected value of process variance (“EVPV”) by a variance ofhypothetical mean (“VHM”) as well as the number of zipcodes in thelargest cluster to determine the number of clusters to use. In general,the EVPV/VHM is larger when variance within a cluster is larger and whenvariance between clusters is larger. This is shown graphically in FIG.10, where a chart 1000 is shown. The chart 1000 represents hypotheticalEVPV/VHM values plotted against a number of clusters. As shown, thereexists a point 1010 at which the value of increasing clustersdiminishes. In some embodiments, the number of clusters to select forterritory factors in this example is the number of clusters at point1010. Selecting more clusters than this would result in unnecessarycomplexity, selecting fewer clusters may result in territory groups withreduced accuracy.

Once a desired “clustering” of territories has been achieved, processingmay continue at step 312 of FIG. 3A, where the territories may beassigned rating factors and loaded into the ratings database 120 for usein rating and pricing policies. By grouping contiguous or geographicallyproximate territories having similar loss characteristics, Applicantsachieve improved pricing and reduced complexity. Illustrative examplesof territory maps are shown in FIG. 8 a (which shows smoothed output,e.g., such as the output from step 308 of FIG. 3 a) and FIG. 8 b (whichshows contiguous or clustered territories, e.g., such as the output fromstep 310 of FIG. 3 a).

FIG. 8 a is a graphical representation of a territory map for the Stateof Connecticut. A number of sub-territories are shown, corresponding tozip code areas established by the U.S. Postal Service. As shown, eachzip code area is shaded with a color representing a loss risk associatedwith historical loss data from that zip code area. The map 800 is theresult of the smoothing process described above as step 308 of FIG. 3 a.The sub-territories (or zip code areas) with similar shading havesimilar loss risks and other characteristics (e.g., such as the zip codeareas marked as items 802 and 804), while other sub-territories or zipcode areas have different loss risks and characteristics (e.g., such asthe zip code area marked as item 806). FIG. 8 b is a similar graphicalrepresentation of a territory map for the State of Connecticut. However,in FIG. 8 b, the rating zones have been clustered such that contiguousregions are shown. The contiguous regions represent the clusteringdescribed above (in FIG. 3 b) and the map 810 represents the output thatmay exist at step 310 of FIG. 3 a, such that zip code areas havingsimilar loss characteristics are grouped together. For example, the zipcode areas identified as items 812 and 814 correspond to the same zipcode areas in FIG. 8 a (items 802 and 804), but are now shaded the samecolor (indicating that the zip code areas 812 and 814 have been assignedthe same territory risk factor). That is, areas 812 and 814, as a resultof the analysis of the present invention, have been identified as havingsimilar loss characteristics, such that insurance policies issued toentities in those zip code areas will be priced using the same territoryrisk factor. As shown in FIG. 8 b, the numerous zip code areas inConnecticut have been reduced to clusters or contiguous zones, whereeach zip code area in a cluster is assigned the same territory riskfactor.

Each type of coverage (e.g., property hurricane, property non-hurricane,and prem/ops) may have a separate territory map and may have separateterritory rating factors. Pursuant to some embodiments, these territorymaps help to visually depict how the boundaries can make sense comparedto the risk related data that resulted in their development. Forexample, there will be more territories in large cities with highpopulation and crime rates. Unique territories could also exist becauseof different temperature and/or rainfall patterns. By aligning ratingareas with territorial regions, Applicants have discovered thatdemographic and other data may be reliably analyzed (in conjunction withhistorical loss data) to create rating tables that more closely matchthe loss risks associated with businesses in those territories. Theresult is an ability to quickly and accurately generate pricing data forbusiness insurance policies, given a business location. Business ownersenjoy more accurate pricing of policies, with many business ownersenjoying lower rates based on this greater accuracy.

Pursuant to some embodiments, the process of FIG. 3 may include somefurther review and analysis of the territory boundaries. For example, insome embodiments, further review may include human review (e.g., byfield office staff or the like) to determine whether fewer or additionalboundaries might be needed to align with local knowledge or otherconsiderations. Once the further review and analysis is complete, theterritory boundaries and definitions and associated territory ratingfactors may be finalized for production use in rating prospectivebusiness insurance policies. The processing at 312 may include updatingthe rating database to include territory rating factors for eachcoverage type and territory set may be generated. For example, territoryrating factors may be generated for each U.S. State in which thebusiness insurance policies are to be issued. Each territory ratingfactor may follow the territory breakdown for that State, and territoryrating factors may be generated for each coverage type in each territoryfor the State. A typical rating sheet may include, for example, a numberof territories, a number of products, and a number of rating factors.

Pursuant to some embodiments, a series of update and maintenanceroutines may be performed to ensure that the territory definitionsremain accurate. For example, on a regular or as-needed basis, a processmay be performed to determine whether any changes to zip codes haveoccurred requiring updates to the territory definitions. As a specificexample, in some situations the U.S. Postal Service may revise (e.g.,add or remove) zip codes. A maintenance process may be performed toidentify such changes and to apply the changes to the productionterritory assignment tables. In some embodiments, the productionterritory assignment tables may be updated on an annual basis (or withevery rate review). In some embodiments, some or all of the steps ofprocess 300 may be performed to update or maintain the rating tables.

Once the rating database has been updated with one or more ratingtables, (and rating formulas and rating factors including territoryrating factors have been updated as will be discussed below), theprocessing platform 102 may be accessed by one or more agents operatingagent devices 130 to request quotes on business insurance policies usingthe rating database. A quoting process 400 will now be described byreference to FIG. 4.

The quoting process 400 may be performed using the rating and quotingsystem 100 of FIG. 1 to rate and quote business insurance policies usingfeatures of the present invention. Quoting process 400 begins at 402where a quote request is received by the processing platform 102.Pursuant to some embodiments, quote requests may be received, forexample, from agents operating agent devices 130. Agent devices 130 maybe computers in communication with processing platform 102 over anetwork connection. In some embodiments, agent devices 130 may becomputers with a Web browser which connect to processing platform 102via an Internet connection. Agents operating agent devices 130 may entera number of data elements specifying the characteristics of the businessfor which they are seeking a business insurance policy quote. Forexample, the agent may enter data specifying the business name, address,business type, and other information. This information is used by theprocessing platform 102 to generate a quote using the rating tablescreated as described above.

Processing continues at 404 where the processing platform 102 analyzesthe business information from the quote request to identify one or moreapplicable coverage(s) for the business. For example, the applicablecoverage(s) may depend on factors such as the business type, whether thebusiness owns property, or the like. Once the applicable coverage(s) areidentified, processing continues at 406 where one or more relevantformula(s) corresponding to the applicable coverage(s) are identified.As an example, processing at 406 may include identifying one or more ofthe following formulas as relevant: building coverage, business personalproperty, personal property of others, products, pre-ops, businessinterruption, and equipment breakdown. Each of these formulas may bestored at, or otherwise accessible to, the quoting module 114 ofplatform 110.

Once applicable formulas have been identified, processing continues at408 where the formula(s) are used to query the rating database 120 toretrieve the premium ratings for the business. For example, in quoterequest involving a business having a property to be covered, thebuilding coverage formula may be selected at 406, and then applied at408 to retrieve the current rates for a property having the valuespecified in the quote request in the territory specified. Each formulamay have a number of factors, multipliers and other data look upsrequired to retrieve and apply the relevant rate from the ratingdatabase 120. However, since the rating database 120 and rating formulasinclude the application of territory factors based on discretegeographical areas, the relevance of each premium corresponds tightly tothe data in the quote request, allowing business insurance policies tobe quickly and accurately quoted using embodiments of the presentinvention.

Once all the rating data has been retrieved from the ratings database120 and applied to the relevant formulas, a quote is constructed at 410.Each quote may involve multiple queries to the ratings database as eachquote may involve the application of multiple ratings formulas. In someembodiments, the quote is constructed after all of the ratings formulashave been applied. Once the quote is completed, the quote is deliveredto the agent device 130 at 412. The agent may then present the quotepackage to the business owner for review and possible binding.

Embodiments of the present invention simplify the process for generatingquotes for business insurance policies by reducing the number of stepsand manual lookups typically required by agents. Further, the quotesprovided are more accurate and correspond to the nature of the loss riskassociated with businesses as the ratings data is constructed fromactual historical loss data and other demographic and territorial data.

Process 400 may be repeated until a quote package has been constructedthat satisfies the agent and the business owner.

Pursuant to some embodiments, one or more optional coverages (e.g., suchas an accounts receivables policy, which may be optionally added bycertain qualifying businesses) may also be calculated as a part of thequoting process. For example, in some embodiments, an optional coveragenet rating may be calculated. Pursuant to some embodiments such optionalcoverage net rating may be calculated in an automated fashion usingautomated processing platform 102 of FIG. 1. In some embodiments, theoptional coverage net rating may be calculated upon request by an agent,or automatically in response to a quote request (such as the quoterequest received at 402 of FIG. 4). Calculation of optional coverage netrating will now be described by reference to FIG. 5, where a process 500is shown that may be implemented using the automated processing platform102 of FIG. 1.

Process 500 begins at 502 where the base policy is priced (e.g., inconjunction with the process 400 of FIG. 4). Pursuant to someembodiments, pricing and rates for optional coverage are performed as afunction of an insured's underlying base coverage for the policy (e.g.,an optional accounts receivables coverage is a function of theunderlying contents coverage rating). That is, pursuant to someembodiments, the optional coverage is priced as a direct function of thepricing of the underlying policy. In this manner, the rating variablesare considered in pricing the optional coverage, without additionalunderwriting or analysis. For example, in some embodiments, once basecoverage has been determined for a business, the underlying ratingvariables have been created. To price an optional coverage, an agent maysimply interact with an agent terminal to quickly obtain pricing for theoptional coverage.

Pricing the optional coverage continues at 504 where the basic premium(or the net premium, where the net premium is the net premium of thepredominant location on the policy) associated with the property isobtained (from the original underwriting analysis). Next, at 506 the netrate is computed by first identifying the value of the property (e.g.,from the information entered for the base policy application). The valueof the property is divided by the premium to arrive at the net rate.Processing continues at 508 where the net rate is multiplied by theamount of the optional endorsement to arrive at a monthly premium amountfor the optional coverage. As an illustrative example, assume an insuredproperty has a value of $8 Million, and the base policy premium is $80k, resulting in a net rate of 0.01. If the building owner wishes toobtain an optional $30 k endorsement, the monthly premium for theendorsement is $30 k*0.01 or $30. The quote for the optional coverage isthen delivered at 510. Applicants have discovered that such an optionalcoverage net rating method ensures that rating variables are “built in”to the optional coverage pricing and allow the efficient, automated, andpredictable pricing of optional coverages.

Pursuant to some embodiments, fees associated with other underwritingexpenses are assigned to individual policies based on industry, risk,and coverage composition factors, allowing expenses to be fairly andpredictably be allocated among policy holders. Applicants recognizedthat a number of “other” underwriting expenses are associated withunderwriting business insurance policies, and that the fair andpredictable allocation of those other underwriting expenses is required.Pursuant to some embodiments, expenses are split to fixed and variable.Applicants have recognized that the fixed fee burden from a given policyvaries with the complexity of the insured based on the likelihood ofincurring claim adjuster time and travel expense, ADM validationrequirements, loss control report order occurrences and call centervolume.

An expense fee calculation process 600 is shown in FIG. 6. Process 600begins at 602 where the base policy is priced (e.g., using the process400 of FIG. 4). At 604 the automated processing platform 110 operates(e.g., using the quoting module 114) to determine the likelihood of theparticular policyholder (or potential policyholder) incurringadministrative expenses. The likelihood may be determined, for example,by consulting expense fee tables stored in rating database 120. Pursuantto some embodiments, the expense fee tables include allocationsdeveloped using both generalized linear model (“GLM”) frameworks topredict the likelihood of expense occurrence and actual expenseinformation. As an example, in some embodiments, the followingallocations are used: (1) pure overhead expenses are allocated as afixed fee per policy, (2) administration validation expenses areallocated based on a calculated likelihood of validation, (3) losscontrol survey expenses are allocated based on a calculated likelihoodof a survey being required, (4) administration expenses are allocatedbased on claim handler salary and expense information, and (5) billingexpenses are allocated based on call center volume.

For those allocations (such as items (2)-(3), above) requiring acalculated likelihood, processing continues at 606 and embodimentsutilize actual cost information in conjunction with a predictive model(such as a GLM) to determine the likelihood that a particular policyholder requiring a particular expense. The result is an expense feecalculation that fairly and reliably applies other underwriting expensesacross policies. The expense fee is used to update the quote andreturned to the agent device for delivery to the business owner orrepresentative.

FIG. 7 is a block diagram of an insurance apparatus 700 in accordancewith some embodiments of the present invention. The apparatus 700 might,for example, comprise a platform or engine similar to the platform 110illustrated in FIG. 1. The apparatus 700 comprises a processor 710, suchas one or more INTEL® Pentium® processors, coupled to a communicationdevice 720 configured to communicate via a communication network (notshown in FIG. 7). The communication device 720 may be used to exchangequote requests and quotes, for example, with one or more remote devices(e.g., such as the agent devices 130 a-n of FIG. 1) and to retrieve orreceive data from third party data sources (e.g., such as data sources140 a-n of FIG. 1).

The processor 710 is also in communication with an input device 740. Theinput device 740 may comprise, for example, a keyboard, a mouse, orcomputer media reader. Such an input device 740 may be used, forexample, to enter information about existing or proposed policy ratingmethodologies, rating schedules, or the like. The processor 710 is alsoin communication with an output device 750. The output device 750 maycomprise, for example, a display screen or printer. Such an outputdevice 750 may be used, for example, to provide reports and/or displayinformation associated with policy rating methodologies, ratingschedules, quotes, or the like.

The processor 710 is also in communication with a storage device 730.The storage device 730 may comprise any appropriate information storagedevice, including combinations of magnetic storage devices (e.g., harddisk drives), optical storage devices, and/or semiconductor memorydevices such as Random Access Memory (RAM) devices and Read Only Memory(ROM) devices.

The storage device 730 stores a program 715 for controlling theprocessor 710. The processor 710 performs instructions of the program715, and thereby operates in accordance any embodiments of the presentinvention described herein. For example, the processor 710 may determineapplicable coverages associated with a quote request received from anagent device. The processor 710 may also identify relevant formula(s) tobe used in constructing and delivering a quote for a new or updatedpolicy. The processor 710 may also calculate and deliver pricing for oneor more optional coverages and/or expense fees associated with a policy.

As used herein, information may be “received” by or “transmitted” to,for example: (i) the insurance apparatus 700 from other devices; or (ii)a software application or module within the insurance apparatus 700 fromanother software application, module, or any other source.

As shown in FIG. 7, the storage device 730 also stores or is otherwisein communication with a rating data 120 (e.g., such as the database 120of FIG. 1). Any number of other databases or database arrangements couldbe employed besides those suggested by the figures. For example,different databases associated with different types of ratingmethodologies, businesses, or policies might be associated with theapparatus 700.

As a result of the embodiments described herein, improved rate andpricing specificity and flexibility for business insurance policies maybe achieved. Further, embodiments allow some or all steps associatedwith the quoting process to be automated, thereby reducing errors andimproving efficiency of the quoting process. Embodiments establish baserates by coverage, amount of insurance relativities, territories andusing an enhanced pricing model based on historical loss data andcurrent demographic, geographical and other data.

The following illustrates various additional embodiments of theinvention. These do not constitute a definition of all possibleembodiments, and those skilled in the art will understand that thepresent invention is applicable to many other embodiments. Further,although the following embodiments are briefly described for clarity,those skilled in the art will understand how to make any changes, ifnecessary, to the above-described apparatus and methods to accommodatethese and other embodiments and applications.

Although specific hardware and data configurations have been describedherein, not that any number of other configurations may be provided inaccordance with embodiments of the present invention (e.g., some of theinformation associated with the databases described herein may becombined or stored in external systems).

Pursuant to some embodiments, features from the territorial analysisdescribed above may be extended to model and create classificationstructures for catastrophe-related losses (e.g., such as hurricanes orthe like). As an initial step, classification structures for catastropheand non-catastrophe losses were modeled separately (e.g., using GLMtechniques). Then, pursuant to some embodiments, to create a total lossclassification system, the total of the modeled results for thecatastrophe and non-catastrophe losses was further modeled. Applicantsrecognized that such an approach allowed a more granular analysis andthe creation of accurate and relevant results for territories (such asStates or other geographical regions) with catastrophe exposure. In thismanner, catastrophe losses may be included in premiums in proportion tonon-catastrophe premiums.

Applicants have discovered that embodiments described herein may beparticularly useful in connection with business insurance products.Note, however, that other types of insurance products may also benefitfrom the invention. For example, embodiments of the present inventionmay be used in conjunction with the rating, pricing and quoting ofpersonal lines policies, homeowners policies, and other types ofbusiness insurance policies. Each of these different types of insurancepolicies may benefit from the use of the territory and other ratingapproaches described herein.

The present invention has been described in terms of several embodimentssolely for the purpose of illustration. Persons skilled in the art willrecognize from this description that the invention is not limited to theembodiments described, but may be practiced with modifications andalterations limited only by the spirit and scope of the appended claims.

1. An apparatus to rate and price business insurance policies,comprising: a communication device to receive information associatedwith a business to be insured; a processor coupled to the communicationdevice; and a storage device in communication with said processor andstoring instructions adapted to be executed by said processor to:receive a quote request associated with a business, the quote requestspecifying a business type and a business location; identifying at leastfirst applicable coverage; based on said at least first applicablecoverage, identifying at least a first relevant coverage formulaincluding at least a first territory factor, said at least firstterritory factor based on a geographical location of said businesswithin a State; query a rating database using said at least firstrelevant coverage formula, said business type and said businesslocation, said query resulting in at least a first price for said atleast first applicable coverage; and transmit a response to said quoterequest, said response including said at least first price and said atleast first applicable coverage.
 2. The apparatus of claim 1, whereinsaid at least first territory factor is selected based on a zip codeassociated with said business location.
 3. The apparatus of claim 1,wherein said at least first territory factor comprises a plurality ofzip codes having at least one of a similar loss exposure characteristicand a similar demographic characteristic.
 4. The apparatus of claim 1,wherein said at least first relevant coverage formula is at least one ofa building rating formula, a business personal property rating formula,a personal property of others rating formula, a product rating formula,a PREM/OPS formula, a business interruption rating formula, an equipmentbreakdown formula, a policy expense fee rating formula, and an optionalcoverage rating formula.
 5. The apparatus of claim 1, wherein saidstorage device further stores instructions adapted to be executed bysaid processor to identify a coverage type associated with said quoterequest, said coverage type at least one of a building, contents,business income, and liability.
 6. The apparatus of claim 1, whereinsaid storage device further stores instructions adapted to be executedby said processor to calculate a premium for an optional coverageassociated with said quote request, said calculation comprisinginstructions to: identify a value of a property associated with saidquote request; calculate a net rate associated with said property andsaid basic premium; calculate a premium for the optional coverage as theproduct of said net rate and said optional coverage amount; and transmitsaid premium for said optional coverage with said response to said quoterequest.
 7. The apparatus of claim 1, wherein said storage devicefurther stores instructions adapted to be executed by said processor tocalculate a policy expense fee associated with said quote request, saidcalculation comprising instructions to: determine a likelihood of thebusiness incurring an administrative expense; calculate, based on saidlikelihood, a policy expense fee; and transmit said policy expense feewith said response to said quote request.
 8. An apparatus to createterritory factors for use in pricing insurance policies, comprising: acommunication device to receive historical loss data, zip code data anddemographic data; a processor coupled to the communication device; and astorage device in communication with said processor and storinginstructions adapted to be executed by said processor to: analyze saidhistorical loss data, zip code data, and demographic data to identifyone or more significant geographical variables and risk characteristicdata; adjust said historical loss data to remove the effects of saidsignificant geographical variables and said risk characteristic data;smooth said adjusted historical loss data using a smoothing algorithm;combine said smoothed data with said significant geographical variablesand said risk characteristic data; iteratively cluster said smootheddata into zip code groups having similar risk exposure data; and assigna territory factor to each of said zip code groups, each of saidterritory factors for use in pricing insurance policies issued toinsured entities located in said zip codes.
 9. The apparatus of claim 8,wherein said insurance policies are selected from the group consistingof: a business insurance policy, a personal lines insurance policy, anda homeowners insurance policy.
 10. The apparatus of claim 8, whereinsaid instructions adapted to be executed by said processor toiteratively cluster said smoothed data further comprises instructionsto: conduct a first analysis of each of said zip codes in a State andsaid risk exposure data to identify a first set of zip codes proximateeach other having similar risk exposure data.
 11. The apparatus of claim10, wherein said instructions adapted to be executed by said processorto iteratively cluster said smoothed data further comprises instructionsto: conduct a second analysis of each of said zip codes in a State andsaid risk exposure data to identify a second set of zip codes proximateeach other having similar risk exposure data; and compare said firstanalysis to said second analysis to determine a most appropriategrouping of zip codes.
 12. The apparatus of claim 11, wherein saidinstructions adapted to be executed by said processor to iterativelycluster said smoothed data further comprises instructions to: conduct athird analysis of each of said zip codes in a State and said riskexposure data to identify a third set of zip codes proximate each otherhaving similar risk exposure data; and compare said third analysis withat least one of said first analysis and second analysis to determine amost appropriate grouping of zip codes.
 13. A system for generatingrating tables, comprising: an analysis module to analyze historical lossdata, zip code data, and demographic data to identify one or moresignificant geographical variables and risk characteristic data for aterritory; a data smoothing module, to remove said one or moresignificant geographical variables and risk characteristic data to forman intermediate data set, to apply a smoothing algorithm to saidintermediate data set forming a smoothed intermediate data set, and torecombine said one or more significant geographical variables and riskcharacteristic data with said smoothed intermediate data set to form asecond intermediate data set; a clustering module, to iterativelycluster said second intermediate data set into zip code groups havingsimilar risk exposure data and to assign a territory factor to each ofsaid zip code groups; and a rating database, for storing said zip codegroups and said territory factors for use in pricing insurance policiesissued to insured entities located in said zip codes.
 14. The system ofclaim 13, wherein said analysis module receives data from one or moredata sources including at least one of a zip code database, a censusdatabase, a historical loss database, and a climate database.
 15. Thesystem of claim 13, further comprising: a quoting module, for receivinga quote request from an agent terminal, said quoting module queryingsaid rating database to receive a territory factor associated with saidquote request.
 16. The system of claim 15, wherein said quoting modulecauses the selection of at least one coverage formula associated withsaid quote request, the at least one coverage formula comprising atleast one of a building rating formula, a business personal propertyrating formula, a personal property of others rating formula, a productrating formula, a PREM/OPS formula, a business interruption ratingformula, an equipment breakdown formula, a policy expense fee ratingformula, and an optional coverage rating formula.
 17. The system ofclaim 15, wherein said querying said rating database is triggered bysaid selection of at least one coverage formula.
 18. The system of claim15, wherein said quoting module causes the selection of a policy expensefee rating formula, the policy expense fee rating formula causing aquery of said ratings database to retrieve a policy expense feecalculated based on a likelihood of an entity incurring anadministrative expense.
 19. The system of claim 15, wherein said quotingmodule causes the selection of an optional coverage rating formula, theoptional coverage rating formula causing the calculation of a premiumfor the optional coverage as the product of a net rate associated withthe quote request and an optional coverage amount.
 20. The system ofclaim 15, wherein said quote request is for a quote for an insurancepolicy are selected from the group consisting of: a business insurancepolicy, a personal lines insurance policy, and a homeowners insurancepolicy.